Daily Peak Demand Research Articles

Socioeconomic characteristics versus density changes: the operational effects of population dynamics on water systems

ABSTRACT While population density can change the overall water demand of a neighborhood, socioeconomic shifts can change the temporal distribution of daily peak demands. This study seeks to determine the comparative criticality of the impacts of both types of population dynamics on the operations of a water system—as measured by pressure. This study specifically assesses scenarios of gentrification, associated with increases in higher-income populations and density changes. Researchers carried out hydraulic simulations of gentrification scenarios using an existing water system in a gentrifying U.S. city. The results indicate that in gentrifying cities such as Portland and Minneapolis, the impact of the socioeconomic shifts may be negligible when compared that of density changes. In gentrifying cities such as Washington D.C. and Atlanta, however, socioeconomic shifts cannot be overlooked. Given the complexity of population dynamics such as gentrification, we recommend decision-makers to carefully assess these dynamics to enhance urban resilience.

An adaptive optimal monthly peak building demand limiting strategy considering load uncertainty

Peak demand limiting is an efficient means to reduce the electricity cost during a billing cycle in cases where peak demand charge is applied. Most previous studies focus on the daily peak demand limiting without considering load uncertainty, which is a big challenge in making proper and reliable decisions in applications. This paper presents an adaptive optimal peak building demand limiting strategy in a month considering load uncertainty. The core element and major innovation of the strategy is the optimal threshold resetting scheme, which involves two major functions as follows. The uncertain economic benefits (i.e., gains and losses) of a demand limiting control are quantified on the basis of probabilistic load forecasts. The optimal monthly limiting threshold is identified using the expectation metric based on the quantified economic benefits. The strategy optimizes and updates the monthly limiting threshold by adapting it to the ever-changing weather forecast and actual peak power use. Case studies are conducted and the results show that this strategy can effectively reduce the monthly peak demand cost under load uncertainty in different seasons. In addition, sensitivity analysis on the cost benefits of the developed strategy using different means of demand limiting and under different electricity demand charges is conducted.

Smart Operation of Electric Vehicles With Four-Quadrant Chargers Considering Uncertainties

Given the expected impact of electric vehicle (EV) charging on power grids, this paper presents a novel two-step approach for the smart operation of EVs with four-quadrant chargers in a primary distribution feeder, accounting for the uncertainties associated with EVs, and considering the perspectives of both the utility and the EV owners. In the first step of the proposed approach, the mean daily feeder peak demand and corresponding hourly feeder control schedules, such as taps and switched capacitor setpoints, considering the bidirectional active and reactive power transactions between EVs and the grid, are determined. A nonparametric bootstrap technique is used, in conjunction with a genetic algorithm-based optimization model, to account for EV uncertainties and discrete variables. In the second step, the maximum possible power that can be given to connected EVs at each node, while providing active and/or reactive power to maintain the peak demand value and corresponding feeder dispatch schedules defined in the first step, is computed every few minutes in a way which is fair to the EVs. The proposed approach is validated using the distribution feeder model of a real primary feeder in Ontario, Canada, considering significant EV penetration levels. The results show that the proposed approach could be implemented in practice to properly operate EVs, satisfying feeder, and peak demand constraints, which would be better than the business-as-usual practice or a popular heuristic method in terms of number of tap operations, system peak demand, and voltage regulation.

Optimization and cost-benefit assessment of hybrid power systems for off-grid rural electrification in Ethiopia

Standalone solar photovoltaic systems are increasingly being distributed in Ethiopia, but these systems are sub-optimal due to their intermittent power supply. A hybrid system that integrates and optimizes across solar photovoltaic and complementary energy sources, such as wind and diesel generation, can improve reliability, and reduce the unit cost of power production. This study assesses the potential of a hybrid system to electrify a remote rural village in Ethiopia. The Hybrid Optimization of Multiple Electric Renewables model is used to assess primary data, develop a load profile and identify the optimal least-cost system option for the village. A sensitivity analysis was performed to determine the effect of variations in solar radiation, wind speed, and diesel price on optimal system configurations. The results show that a hybrid system with a combination of photovoltaic array, wind turbine, battery and diesel generator is the best option from an economic point of view. To meet the village's daily peak demand of 19.6 kW, energy generation cost is estimated at 0.207 dollars per kilowatt hour and net present cost at 82,734 dollars. The optimal system allows for a reduction of 37.3 tons of carbon dioxide emissions per year compared with diesel-only electricity generation.

A data-centric bottom-up model for generation of stochastic internal load profiles based on space-use type

There is currently no established methodology for the generation of synthetic stochastic internal load profiles for input into building energy simulation. In this paper, a Functional Data Analysis approach is used to propose a new data-centric bottom-up model of plug loads based on hourly data monitored at a high spatial resolution and by space-use type for a case-study building. The model comprises a set of fundamental Principal Components (PCs) that describe the structure of all data samples in terms of amplitude and phase. Scores (or weightings) for each daily demand profile express the contribution of each PC to the demand. Together the principal components and the scores constitute a structure-based model potentially applicable beyond the building considered. The results show good agreement between samples generated using the model and monitored data for key parameters of interest including the timing of the daily peak demand.

Estimating the daily peak and annual total electricity demand for cooling in Vienna, Austria by 2050

Climate change is expected to increase temperatures worldwide and exacerbate urban heat load due to the urban heat island effect. Urban populations will be more exposed to climate change impacts on human health and mortality as compared to citizens living in rural areas. To adapt, urban populations will increase the use of air conditioning and an increase in electricity consumption for cooling is forecast.We use a top-down method, based on the hourly electricity consumption and daily temperatures for years 2015 and 2016 for 19 European countries, to estimate the future annual demand and daily peak demand for cooling in Vienna, Austria, until 2050. The estimation is based on an ensemble average of seven downscaled climate models under two climate scenarios (RCP4.5 and RCP8.5) and includes a factor for the increase in air conditioning penetration with climate change. The estimate of peak demand, inclusion of penetration and application to a locality make the study somewhat novel.Our results suggest that annual electrical energy for cooling in Vienna will increase from the current amount of 22 GWh/year to 95 (33–189) GWh/year by 2050 – with little difference during this time frame between RCP4.5 and RCP8.5. During the same period, peak electrical energy demand will increase to around 117 (64–191) MWh/day in 2050 from its current value of 65.5 MWh/day.

Submarine power cable between Europe and North America: A techno-economic analysis

Abstract Increasing the deployment of power generators using variable renewable power sources, such as wind and solar, brings power balancing challenges in electricity systems. One mean to achieve power balancing and to share balancing resources, is to interconnect electricity systems to geographically aggregate remotely located variable power sources. This article addresses this potential and provides insights for electricity trading through a submarine power cable between Europe and the eastern part of North America. While such ambitious power interconnection concepts appear in recent literature, this article is the first to present a techno-economic analyses. This article concludes that, a 4000 MW cable between Europe and North America could bring an annual socio-economic benefit of 177 M€ in 2030. In addition to the differences in generation costs, mutual benefits from electricity trading between Europe and North America derive from different daily peak demand times, low correlation in generation from renewable energy sources, and in seasonal demand variations. The results of the cost-benefit analysis indicate that the benefit for society is sufficient to cover the investment costs. Thus, the proposed interconnector is welfare improving.

Siting and sizing of distributed energy storage to mitigate voltage impact by solar PV in distribution systems

This work explores the allocation question of battery energy storage systems (BESS) in distribution systems for their voltage mitigation support in integrating high penetration solar photovoltaics (PV). A genetic algorithm (GA)-based bi-level optimization method is developed that reduces the voltage fluctuations caused by PV penetration through deploying BESS among permitted nodes of a distribution system while accounting for their capital, land-of-use and installation costs using a qualitative cost model. The optimization problem considers BESS capacity and installation points in the distribution system as decision variables. Each BESS operation is determined using a linear programming (LP) routine that minimizes the daily coincident peak demand. A comprehensive validation study is carried out through exhaustive enumeration with the IEEE 8500-Node test feeder showing that the proposed method results in consistent decisions that appear to be globally optimal. Further sensitivity studies are conducted to showcase the behavior of the method under varying sizing costs, siting costs and PV penetrations.

A novel fuzzy control algorithm for reducing the peak demands using energy storage system

Commercial and industrial customers are subject to the monthly maximum demand charges which can be as high as 30% of the total electricity bills. Battery-based energy storage system (BESS) can be used to reduce the monthly maximum demand charges. A number of control strategies have been developed for the BESS to reduce the daily peak demands. A fuzzy control algorithm is developed to reduce the daily peak demands with the limited capacity of the BESS. Its performance is evaluated at two different buildings, namely building A and B. The fuzzy controller forecasts the load profile one day in advance using the historical load data. Then during the day of peak reduction, the controller will adjust the power output of BESS using the latest state of charge and operation time. The performance of the fuzzy controller is compared with other two controllers developed in the past. The experimental results show that fuzzy controller is the most effective approach for the peak reduction under the limited capacity of the BESS.

Demand-side management for supply-side efficiency: Modeling tailored strategies for reducing peak residential water demand

Increasingly, the water sector is exploring the value of applying demand management strategies to reduce peak water use through behavioural and technical solutions. Literature suggests that using behavioural interventions may be a useful approach in changing the daily peak demand patterns to reduce the pressure on network pumping energy costs during peak use times. There is a lack of studies, however, that have investigated the role of social based marketing or behavioural intervention studies on specifically reducing and shifting residential peak diurnal daily water end-use demand. This concept is modelled in this current study through the application of longitudinal experimental end-use data to predict how reduced demand through behaviour change can impact on overall peak residential demand. Notwithstanding the acknowledged limitations of the study, results illustrate a range of potential peak hour flow savings that can be realised from reducing total demand, or shifting the peak demand in households. The study provides preliminary evidence that water businesses can use demand-side strategies to also achieve efficiencies in the distribution of urban water (e.g. reduced energy for pumping in pressurised water system, pipe augmentation deferrals, peak energy demands).

Review of UK Domestic Electricity Consumption and Potential Trends in Using Renewable Energy Sources and Plug-in Hybrid Electrical Vehicles

This paper presents a review of electricity profiles in UK households and their electricity demands as well as new trends and solutions for energy consumption. There are two aspects investigated in this research; the first deals with new trends and development in the field of technology, which includes usage of electric vehicles. The Second aspect is ecological, of which the UK is trying to reduce their energy demands and CO2 emission in order to find better solutions for energy problem. Also, this paper investigates renewable energy sources and new technology trends in changeable market electricity prices. The lack of information in daily electricity usage outline per household is a key point on which a new solution will be proposed. Original data are collected from daily energy consumption and energy peak demands from UK electricity providers. Furthermore, the effect of energy usage by electric vehicle is just recently introduced into UK and considered as part of overall development. UK has introduced new solutions for electricity usage, involving renewable energy supplies, mostly solar panels, as best way to meet the increase in electricity demands as well as electricity bills. Data shows that solar energy power is more and more used, which is procured with lower PV prices during manufacturing process and their installation.

Energy Forecasting for Event Venues: Big Data and Prediction Accuracy

Advances in sensor technologies and the proliferation of smart meters have resulted in an explosion of energy-related data sets. These Big Data have created opportunities for development of new energy services and a promise of better energy management and conservation. Sensor-based energy forecasting has been researched in the context of office buildings, schools, and residential buildings. This paper investigates sensor-based forecasting in the context of event-organizing venues, which present an especially difficult scenario due to large variations in consumption caused by the hosted events. Moreover, the significance of the data set size, specifically the impact of temporal granularity, on energy prediction accuracy is explored. Two machine-learning approaches, neural networks (NN) and support vector regression (SVR), were considered together with three data granularities: daily, hourly, and 15minutes. The approach has been applied to a large entertainment venue located in Ontario, Canada. Daily data intervals resulted in higher consumption prediction accuracy than hourly or 15-min readings, which can be explained by the inability of the hourly and 15-min models to capture random variations. With daily data, the NN model achieved better accuracy than the SVR; however, with hourly and 15-min data, there was no definitive dominance of one approach over another. Accuracy of daily peak demand prediction was significantly higher than accuracy of consumption prediction.

Modelling Summer Daily Peak Loads in South Africa Using Discrete Time Markov Chain

Electricity demand exhibits a large degree of randomness in South Africa, particularly in summer. Its description requires a detailed analysis using statistical methodologies, in particular stochastic processes. The paper presents a Markov chain analysis of peak electricity demand. The data used is from South Africa's power utility company Eskom, for the period 2000 to 2011. This modelling approach is important to decision makers in the electricity sector particularly in scheduling maintenance and refurbishments of power-plants. The randomness effect is accountable to meteorological factors and major electricity appliance usage. Aggregated data on daily electricity peak demand is used to develop the transition probability matrices, steady-state probabilities, mean return- and the first passage times. Such analysis are important to Eskom and other energy companies in planning load-shifting, load analysis and scheduling of electricity particularly during peak period in summer.

Modeling and integration of a heat storage tank in a compressed air electricity storage process

In an adiabatic compressed air energy storage process (A-CAES), heat storage tank operation is a key factor that determines the overall energy performance of the process. To highlight energy issues linked to a correct tank design in the specific case of an A-CAES system, a two-dimensional thermal numerical model was developed. Thermal efficiencies of the tank are presented with abacus defined from the four dimensionless numbers defining the thermal behavior of the reservoir. Cycling effects are explored with a realistic case study corresponding to an A-CAES system design to deliver an electrical power of 250MWel for 4h, the daily peak demand. Extended beyond the thermal reservoir, A-CAES thermodynamic analysis combined with the dynamic simulation makes it possible to generate a direct quantitative link between reservoir sizing and A-CAES global efficiency.

Quantile Regression for Peak Demand Forecasting

We demonstrate that annual peak demand days are characterized by both extreme values of predictors (such as weather) and large unpredictable shocks to demand. OLS approaches incorporate the former feature, but not the latter, leading OLS to produce downwardly-biased estimates of the annual peak. We develop a new estimation procedure, optimal forecast quantile regression (OFQR), that uses quantile regression to estimate a model of daily peak demand, then uses a loss function framework to estimate a quantile to predict the annual peak. We compare the results of the OLS and OFQR estimation approaches for 32 utility zones. While the OFQR approach is unbiased, OLS under-forecasts by nearly 5% on average. Further, OFQR reduces the average absolute percent error by 43%. A bootstrapping procedure generates forecast intervals with accurate 95% coverage in sample and 87% coverage out of sample.

Estimation on Possibility and Capacity of Residential Peak Electricity Demand Reduction by Demand Response Scenario in Rural Areas of Japan

This study analyzes the potential peak demand reduction based on actual measurement result and outage scenarios acceptable to rural residential customers in Japan. To obtain the actual demand, load measurement was carried out on 18 households in Tsuru and 8 households in Izu during a week in winter, spring, and summer. The measurement result shows that the maximum demand in both areas occurred in winter. Daily aggregate peak demands extracted from the measurement were then modelled using Lognormal and Weibull distribution to obtain the probability of outage in any system's peak capacity value. On the other hand, to gauge the outage scenario that is acceptable to electricity consumers, an outage scenario representing an extremely rare case was selected from a nationwide survey. The survey result shows that six 2-hour outages per year with annual bill reduction of JPY30,000 is a feasible scenario. The modelling demonstrates that this scenario can result in 10.2% peak demand reduction in Tsuru and 5.2% peak demand reduction in Izu. The result of this study may be beneficial for distributed power system designers to determine optimum power system capacity as well as demand response programs acceptable to rural residential customers.

Electrical Vehicles in the Smart Grid: A Mean Field Game Analysis

In this article, we investigate the competitive interaction between electrical vehicles or hybrid oil-electricity vehicles in a Cournot market consisting of electricity transactions to or from an underlying electricity distribution network. We provide a mean field game formulation for this competition, and introduce the set of fundamental differential equations ruling the behavior of the vehicles at the feedback Nash equilibrium, referred here to as the mean field equilibrium. This framework allows for a consistent analysis of the evolution of the price of electricity as well as of the instantaneous electricity demand in the power grid. Simulations precisely quantify those parameters and suggest that significant reduction of the daily electricity peak demand can be achieved by appropriate electricity pricing.

Modeling of Load Demand Due to EV Battery Charging in Distribution Systems

This paper presents a methodology for modeling and analyzing the load demand in a distribution system due to electric vehicle (EV) battery charging. Following a brief introduction to the common types of EV batteries and their charging characteristics, an analytical solution for predicting the EV charging load is developed. The method is stochastically formulated so as to account for the stochastic nature of the start time of individual battery charging and the initial battery state-of-charge. A comparative study is carried out by simulating four EV charging scenarios, i.e., uncontrolled domestic charging, uncontrolled off-peak domestic charging, “smart” domestic charging and uncontrolled public charging-commuters capable of recharging at the workplace. The proposed four EVs charging scenarios take into account the expected future changes to the electricity tariffs in the electricity market place and appropriate regulation of EVs battery charging loads. A typical U.K. distribution system is adopted as an example. The time-series data of EV charging loads is taken from two commercially available EV batteries: lead-acid and lithium-ion. Results show that a 10% market penetration of EVs in the studied system would result in an increase in daily peak demand by up to 17.9%, while a 20% level of EV penetration would lead to a 35.8% increase in peak load, for the scenario of uncontrolled domestic charging-the “worst-case” scenario.

Price, environment and security: Exploring multi-modal motivation in voluntary residential peak demand response

Peak demand on electricity grids is a growing problem that increases costs and risks to supply security. Residential sector loads often contribute significantly to seasonal and daily peak demand. Demand response projects aim to manage peak demand by applying price signals and automated load shedding technologies. This research investigates voluntary load shedding in response to information about the security of supply, the emission profile and the cost of meeting critical peak demand in the customers’ network. Customer willingness to change behaviour in response to this information was explored through mail-back survey. The diversified demand modelling method was used along with energy audit data to estimate the potential peak load reduction resulting from the voluntary demand response. A case study was conducted in a suburb of Christchurch, New Zealand, where electricity is the main source for water and space heating. On this network, all water heating cylinders have ripple-control technology and about 50% of the households subscribe to differential day/night pricing plan. The survey results show that the sensitivity to supply security is on par with price, with the emission sensitivity being slightly weaker. The modelling results show potential 10% reduction in critical peak load for aggregate voluntary demand response.

Prediction of daily peak electricity demand in South Africa using volatility forecasting models

Daily peak electricity demand forecasting in South Africa using a seasonal autoregressive integrated moving average (SARIMA) model, a SARIMA model with generalized autoregressive conditional heteroskedastic (SARIMA–GARCH) errors and a regression-SARIMA–GARCH (Reg-SARIMA–GARCH) model is presented in this paper. The GARCH modeling methodology is introduced to accommodate the possibility of serial correlation in volatility since the daily peak demand data exhibits non-constant mean and variance, and multiple seasonality corresponding to weekly and monthly periodicity. The proposed Reg-SARIMA–GARCH model is designed in such a way that the predictor variables are initially selected using a multivariate adaptive regression splines algorithm. The developed models are used for out of sample prediction of daily peak demand. A comparative analysis is done with a piecewise linear regression model. Results from the study show that the Reg-SARIMA–GARCH model produces better forecast accuracy with a mean absolute percent error (MAPE) of 1.42%.